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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- File Handling 1. Read from and write to files (text, CSV) 2. Use the csv module for basic CSV operations - Data Structures 1. Work with lists, tuples, dictionaries, and sets 2. Apply common operations and methods to data structures - Python Fundamentals 1. Implement control flow structures (loops, conditionals) 2. Use variables, data types, and basic operators 3. Define and use functions
Topic 2: Communicating Insights and Reporting	12.5%	- Data Visualization 1. Select appropriate visuals for different data types 2. Recognize common visualization types (bar, line, pie charts) 3. Interpret simple data visualizations - Data Storytelling and Reporting 1. Create clear and concise analytical reports 2. Structure insights as a narrative 3. Present insights with visual and verbal techniques
Topic 3: Introduction to Data and Data Analysis Concepts	22.5%	- Data Analysis Process and Workflow 1. Describe the steps of the data analysis process 2. Explain the role of data cleaning and preparation 3. Identify common data sources and collection methods - Define and Classify Data 1. Explain how data becomes meaningful 2. Differentiate structured, semi-structured, and unstructured data 3. Classify data as quantitative or qualitative - Data Ethics and Privacy 1. Recognize ethical considerations in data handling 2. Understand basic data privacy concepts
Topic 4: Working with Data and Performing Simple Analyses	32.5%	- Data Cleaning and Transformation 1. Handle missing and inconsistent data 2. Perform basic data transformation (filtering, sorting, grouping) - Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode) - Data Analysis with Python Libraries 1. Work with the datetime module for date/time data 2. Utilize the collections module for specialized containers 3. Use the math and statistics modules for basic calculations 4. Perform basic operations with NumPy arrays

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

A. Creating summary tables and charts to describe patterns in historical data.
B. Developing machine learning algorithms to predict user behavior.
C. Designing a data pipeline to ingest and process real-time sensor data.
D. Recommending pricing strategies using predictive modeling and A/B testing.

Answer: A

Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.

NEW QUESTION # 46
You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics

A. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics
B. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics
C. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics
D. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))

Answer: D

Explanation:
It uses statistics.stdev() to compute the standard deviation, statistics.mean() to compute the average and math.ceil() to round it up to the nearest whole number, and statistics.median() to compute the median.

NEW QUESTION # 47
A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

A. Error
B. Returns None
C. Creates new list
D. Modifies original

Answer: C

Explanation:
Slicing creates a new list object rather than modifying the original. The expression lst[::-1] returns a reversed copy, leaving the original list unchanged.

NEW QUESTION # 48
You are writing a function to validate battery voltage readings. The rules are:
- If the reading is "MISSING"or None, return "Missing".
- If the value is below 2.5or above 4.2, return "Outlier".
Otherwise, return "Normal".
Which version correctly implements this logic using the most robust conditional and nested structures? Select the best answer.

Answer: D

Explanation:
It first handles the non-numeric cases ("MISSING" and None) in a single condition, preventing invalid comparisons, and then cleanly checks the numeric outlier bounds using an or condition (<
2.5 or > 4.2) before defaulting to "Normal".

NEW QUESTION # 49
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

A. Error
B. [1,2,3,[4,5]]
C. [4,5,1,2,3]
D. [1,2,3,4,5]

Answer: B

Explanation:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].

NEW QUESTION # 50
......

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